A high black-hole-to-host mass ratio in a lensed AGN in the early Universe.

Early JWST observations have uncovered a population of red sources that might represent a previously overlooked phase of supermassive black hole growth1-3. One of the most intriguing examples is an extremely red, point-like object that was found to be triply imaged by the strong lensing cluster Abell 2744 (ref. 4). Here we present deep JWST/NIRSpec observations of this object, Abell2744-QSO1. The spectroscopy confirms that the three images are of the same object, and that it is a highly reddened (AV ≃ 3) broad emission line active galactic nucleus at a redshift of zspec = 7.0451 ± 0.0005. From the width of Hß (full width at half-maximum = 2,800 ± 250 km s-1), we derive a black hole mass of M BH = 4 - 1 + 2 × 1 0 7 M ⊙ . We infer a very high ratio of black-hole-to-galaxy mass of at least 3%, an order of magnitude more than that seen in local galaxies5 and possibly as high as 100%. The lack of strong metal lines in the spectrum together with the high bolometric luminosity (Lbol = (1.1 ± 0.3) × 1045 erg s-1) indicate that we are seeing the black hole in a phase of rapid growth, accreting at 30% of the Eddington limit. The rapid growth and high black-hole-to-galaxy mass ratio of Abell2744-QSO1 suggest that it may represent the missing link between black hole seeds6 and one of the first luminous quasars7.

Most of the photons that reionized the Universe came from dwarf galaxies.

The identification of sources driving cosmic reionization, a major phase transition from neutral hydrogen to ionized plasma around 600-800 Myr after the Big Bang1-3, has been a matter of debate4. Some models suggest that high ionizing emissivity and escape fractions (fesc) from quasars support their role in driving cosmic reionization5,6. Others propose that the high fesc values from bright galaxies generate sufficient ionizing radiation to drive this process7. Finally, a few studies suggest that the number density of faint galaxies, when combined with a stellar-mass-dependent model of ionizing efficiency and fesc, can effectively dominate cosmic reionization8,9. However, so far, comprehensive spectroscopic studies of low-mass galaxies have not been done because of their extreme faintness. Here we report an analysis of eight ultra-faint galaxies (in a very small field) during the epoch of reionization with absolute magnitudes between MUV ≈ -17 mag and -15 mag (down to 0.005L⋆ (refs. 10,11)). We find that faint galaxies during the first thousand million years of the Universe produce ionizing photons with log[ξion (Hz erg-1)] = 25.80 ± 0.14, a factor of 4 higher than commonly assumed values12. If this field is representative of the large-scale distribution of faint galaxies, the rate of ionizing photons exceeds that needed for reionization, even for escape fractions of the order of 5%.

Technology for societal change: Evaluating a mobile app addressing the emotional needs of people experiencing homelessness.

New technology that aims to tackle the systemic and societal problems face challenges bringing together diverse stakeholder perspectives effectively. We evaluate how an emotion-led approach, with a Living Lab process may be effective in these situations. We discuss findings and their implications for this in the context of the development and ongoing maintenance of a web app called "Ask Izzy". Ask Izzy supports people who are homeless or are otherwise disadvantaged by providing information and consequently improving their everyday life and wellbeing. We present a mixed-method evaluation of the web app: firstly, we evaluate impact of key design decisions upon engagement. Secondly, we conducted semi-structured interviews with 30 participants who are either homeless, ex-homeless or service providers and used content analysis. Then we demonstrate that our emotion-led approach brings a novel perspective on concerns from key actors influencing the refinement of the app. The results section outlines emotional goals such as a feeling of control that were important to consider in order to meet the needs of both end users and the wider service provision network. Our study provides recommendations and an approach that may inform others in developing and delivering similar health care and related systems and services.

Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence.

BACKGROUND: A patient's health information is generally fragmented across silos because it follows how care is delivered: multiple providers in multiple settings. Though it is technically feasible to reunite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization for this purpose. OBJECTIVES: Machine learning can be conducted in a federated manner on patient datasets with the same set of variables but separated across storage. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more dimensions "confederated machine learning", which we aim to develop in this study. METHODS: We propose and evaluate confederated learning for training machine learning models to stratify the risk of several diseases among silos when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching. The confederated learning method can be intuitively understood as a distributed learning method with representation learning, generative model, imputation method and data augmentation elements. RESULTS: Our confederated learning method achieves AUCROC (Area Under The Curve Receiver Operating Characteristics) of 0.787 for diabetes prediction, 0.718 for psychological disorders prediction, and 0.698 for Ischemic heart disease prediction using nationwide health insurance claims. CONCLUSION: Our proposed confederated learning method successfully trained machine learning models on health insurance data separated by two or more dimensions.

Good Proctor or "Big Brother"? Ethics of Online Exam Supervision Technologies.

Online exam supervision technologies have recently generated significant controversy and concern. Their use is now booming due to growing demand for online courses and for off-campus assessment options amid COVID-19 lockdowns. Online proctoring technologies purport to effectively oversee students sitting online exams by using artificial intelligence (AI) systems supplemented by human invigilators. Such technologies have alarmed some students who see them as a "Big Brother-like" threat to liberty and privacy, and as potentially unfair and discriminatory. However, some universities and educators defend their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This essay provides one of the first sustained moral philosophical analyses of these technologies, focusing on ethical notions of academic integrity, fairness, non-maleficence, transparency, privacy, autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics, and all are relevant to education. The essay discusses these ethical issues. It also offers suggestions for educational institutions and educators interested in the technologies about the kinds of inquiries they need to make and the governance and review processes they might need to adopt to justify and remain accountable for using online proctoring technologies. The rapid and contentious rise of proctoring software provides a fruitful ethical case study of how AI is infiltrating all areas of life. The social impacts and moral consequences of this digital technology warrant ongoing scrutiny and study.

Explainable models for forecasting the emergence of political instability.

Building on previous research on the use of macroeconomic factors for conflict prediction and using data on political instability provided by the Political Instability Task Force, this article proposes two minimal forecasting models of political instability optimised to have the greatest possible predictive power for one-year and two-year event horizons, while still making predictions that are fully explainable. Both models employ logistic regression and use just three predictors: polity code (a measure of government type), infant mortality, and years of stability (i.e., years since the last instability event). These models make predictions for 176 countries on a country-year basis and achieve AUPRC's of 0.108 and 0.115 for the one-year and two-year models respectively. They use public data with ongoing availability so are readily reproducible. They use Monte Carlo simulations to construct confidence intervals for their predictions and are validated by testing their predictions for a set of reference years separate from the set of reference years used to train them. This validation shows that the models are not overfitted but suggests that some of the previous models in the literature may have been. The models developed in this article are able to explain their predictions by showing, for a given prediction, which predictors were the most influential and by using counterfactuals to show how the predictions would have been altered had these predictors taken different values. These models are compared to models created by lasso regression and it is shown that they have at least as good predictive power but that their predictions can be more readily explained. Because policy makers are more likely to be influenced by models whose predictions can explained, the more interpretable a model is the more likely it is to influence policy.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning.

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Evaluation of stress-control layout at the Subtropolis Mine, Petersburg, Ohio.

The Subtropolis room-and-pillar mine extracts the Vanport Limestone (Allegheny Formation, Pennsylvanian System) near Petersburg, Ohio. Strata instability problems associated with excessive concentrations of lateral stress caused the mine operator to implement a change in layout design. This mining method has been identified as a stress control layout and has been used by other underground stone mines in the past with varying degrees of success. Practical experience has shown that entry headings advance in the direction of the principal lateral stress, producing lower stress concentrations with better mining conditions. It is important to minimize stress concentrations along the mining front, so an arrow-shaped advance is recommended. This technique advances more developments (headings) in a "good" direction and reduces developments (crosscuts) in the "bad direction." As is expected, the stress control layout enhances the potential for shear failures in crosscuts. It is, therefore, important to focus crosscut engineering interventions that either: (a) lower stress concentrations (for example, an arched roof) or (b) enhance strength of the strata containing the shears (for example, rock reinforcement). This study focuses on observing strata conditions on a regular basis and monitoring the response of these strata to changing geologic and mining conditions through 3D Dynamic LiDAR scans.

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.

OBJECTIVE: To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. MATERIALS AND METHODS: We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. RESULTS: Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). DISCUSSION: XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals' point of view. CONCLUSION: Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.

The Prognostic and Predictive Value of SOX2+ Cell Densities in Patients Treated for Colorectal Cancer.

SOX2 (sex-determining region-Y homeobox-2) is a transcription factor essential for the maintenance of pluripotency and is also associated with stem-cell-like properties in preclinical cancer models. Our previous study on a cohort of stage III colon cancer patients demonstrated high SOX2+ cell densities were associated with poor prognosis. However, most patients were treated with adjuvant chemotherapy so the prognostic value of SOX2 could not be assessed independently from its value as a predictive marker for non-response to chemotherapy. This study aimed to assess whether SOX2 was a true prognostic marker or a marker for chemotherapy response in a historical cohort of patients, a high proportion of whom were chemotherapy-naïve. SOX2 immunostaining was performed on tissue micro-arrays containing tumor cores from 797 patients with stage II and III colorectal cancer. SOX2+ cell densities were then quantified with StrataQuest digital image analysis software. Overall survival was assessed using Kaplan-Meier estimates and Cox regression. It was found that high SOX2+ cell densities were not associated with poor overall survival. Furthermore, all patients had a significant improvement in survival after 5-fluorouracil (5-FU) treatment, irrespective of their SOX2+ cell density. Therefore, SOX2+ cell densities were not associated with prognosis or chemotherapy benefit in this study. This is in contrast to our previous study, in which most patients received oxaliplatin as part of their treatment, in addition to 5-FU. This suggests SOX2 may predict response to oxaliplatin treatment, but not 5-FU.

Safe Handling of Hazardous Drugs: ASCO Standards.

PURPOSE: To provide 2019 ASCO standards on the safe handling of hazardous drugs. METHODS: An Expert Panel was formed, and a systematic review of the literature on closed system transfer devices was performed to May 2017 using PubMed. The Cochrane Database of Systematic Reviews, PubMed, and Google Scholar were used to search for studies of medical surveillance and external ventilation/health effects of exposure to vapors to November 2017. Available standards were considered for endorsement. Public comments were solicited and considered in preparation of the final manuscript. RESULTS: The search for primary research found no studies that addressed health outcomes as they relate to the identified interventions of interest. The ASCO Expert Panel endorses the best practices for safe handling of hazardous drugs as issued by the Occupational Safety and Health Administration, US Pharmacopeia Chapter 800, and Oncology Nursing Society with clarifications in four key areas: medical surveillance, closed system transfer devices, external ventilation of containment secondary engineering controls or containment segregated compounding areas, and alternative duties. CONCLUSION: The ASCO standards address the need for clear standards concerning safe handling of hazardous oncology drugs. More research is needed in several key areas to quantify the level of risk associated with handling hazardous drugs in current workplace settings where the hierarchy of controls is consistently applied. Additional information is available at www.asco.org/safe-handling-standards .

Expression of PD-L1 and SOX2 during rectal tumourigenesis: Potential mechanisms for immune escape and tumour cell invasion.

Immunoediting is defined as a process whereby tumour cells develop the capacity to escape immune cell recognition. Accumulating evidence suggests that cancer stem-like cells (CSCs) have an enhanced capacity to interact with the immune system. The expression of CSCs and immune cell-associated markers has been demonstrated to change with disease progression from premalignant lesions to invasive cancer. The present study investigated the expression of putative CSC and immune cell-associated markers in different stages of progression from dysplasia to invasive malignancy in rectal lesions. Immunohistochemistry was performed for the CSC markers Lgr5 and SOX2 and the immune-associated markers CD8, Foxp3 and PD-L1 in 79 cases of endoscopically-excised rectal lesions, ranging from low grade adenoma (LG) to invasive adenocarcinoma (AdCa). CD8 and Foxp3 expression significantly increased with advances in disease progression [AdCa vs. LG: Odds ratio (OR) 4.33; 95% confidence interval (CI), 1.16-16.3; P=0.03 and OR, 40.5; 95% CI, 6.57-249.6; P<0.0001, respectively]. An increase in programmed death-ligand 1 (PD-L1) expression was also observed with disease progression (OR, 24.0; 95% CI, 4.23-136.2; P=0.0003). The expression of sex determining region Y-box 2 (SOX2) did not correlate with disease progression, although an elevated expression was observed in areas with high grade dysplasia. Increased PD-L1 expression may be a mechanism by which tumour cells evade immune recognition, facilitating tumour cell invasion in rectal cancer. The expression of SOX2 in areas with high grade dysplasia may indicate the de-differentiation of tumour cells, or the activation of migration pathways for invasion.

Sustainability is possible despite greed - Exploring the nexus between profitability and sustainability in common pool resource systems.

The sustainable use of common pool resources has become a significant global challenge. It is now widely accepted that specific mechanisms such as community-based management strategies, institutional responses such as resource privatization, information availability and emergent social norms can be used to constrain individual 'harvesting' to socially optimal levels. However, there is a paucity of research focused specifically on aligning profitability and sustainability goals. In this paper, an integrated mathematical model of a common pool resource game is developed to explore the nexus between the underlying costs and benefits of harvesting decisions and the sustainable level of a shared, dynamic resource. We derive optimal harvesting efforts analytically and then use numerical simulations to show that individuals in a group can learn to make harvesting decisions that lead to the globally optimal levels. Individual agents make their decision based on signals received and a trade-off between economic and ecological sustainability. When the balance is weighted towards profitability, acceptable economic and social outcomes emerge. However, if individual agents are solely driven by profit, the shared resource is depleted in the long run - sustainability is possible despite some greed, but too much will lead to over-exploitation.

Could the Use of an Enhanced Recovery Protocol in Laparoscopic Donor Nephrectomy be an Incentive for Live Kidney Donation?

INTRODUCTION AND BACKGROUND: Gastrointestinal (GI) recovery after major abdominal surgery can be delayed from an ongoing need for narcotic analgesia thereby prolonging hospitalization. Enhanced recovery after surgery (ERAS) is a multimodal perioperative care pathway designed to facilitate early recovery after major surgery by maintaining preoperative body composition and physiological organ function and modifying the stress response induced by surgical exposure. Enhanced recovery programs (ERPs) in colorectal surgery have decreased the duration of postoperative ileus and the hospital stay while showing equivalent morbidity, mortality, and readmission rates in comparison to the traditional standard of care. This study is a pilot trial to evaluate the benefits of ERAS protocols in living kidney donors undergoing laparoscopic nephrectomy. METHODS: This is a single-center, non-randomized, retrospective analysis comparing the outcomes of the first 40 live kidney donors subjected to laparoscopic nephrectomy under the ERAS protocol to 40 donors operated prior to ERAS with traditional standard of care. Our ERAS protocol includes reduced duration of fasting with preoperative carbohydrate loading, intraoperative fluid restriction to 3 ml/kg/hr, target urine output of 0.5 ml/kg/hr, use of subfascial Exparel injection (bupivacaine liposome suspension), and postoperative narcotic-free pain regimen with acetaminophen, ketorolac, or tramadol. Short-term patient outcomes were compared using Pearsons's Chi-Squared test for categorical variables and the Kruskal-Wallis test for continuous variables. Additionally, a multivariate analysis was conducted to evaluate factors influencing patient length of stay and likelihood of readmission. RESULTS: ERAS protocol reduced the postoperative median length of stay decreased from 2.0 to 1.0 days (p=0.001). Overall pain scores were significantly lower in the ERAS group (peak pain score 6.0 vs. 8.00, p< 0.001; morning after surgery pain score 3.0 vs. 7.0, p=0.001; lowest pain score 0.0 vs. 2.0, p=0.016) despite the absence of postoperative narcotics. The average duration of surgery was shorter in the ERAS group (248 vs. 304 minutes, p<0.001). The average amount of intraoperative fluid used was significantly lower in the ERAS group (2500 ml vs. 3525 ml, p<0.001) without affecting the donor renal function. The incidence of delayed graft function was similar in the two groups (p=0.541). A trend toward lower readmission was noted with the ERAS protocol (12.8% vs. 27.5%, p=0.105). GI dysfunction was the most common reason for readmission. CONCLUSION: Application of an ERAS protocol in a laparoscopic living donor nephrectomy was associated with reduced length of hospitalization and improved pain scores related likely to intraoperative use of subfascial Exparel and a shorter duration of ileus. Restricted use of intraoperative fluids prevents excessive third spacing and bowel edema, enhancing gut recovery without adversely impacting recipient graft function. This study suggests that ERAS has the potential to enhance the advantages of laparoscopic surgery for live kidney donation through optimizing donor outcomes and perioperative patient satisfaction.

Analysis of roof and pillar failure associated with weak floor at a limestone mine.

A limestone mine in Ohio has had instability problems that have led to massive roof falls extending to the surface. This study focuses on the role that weak, moisture-sensitive floor has in the instability issues. Previous NIOSH research related to this subject did not include analysis for weak floor or weak bands and recommended that when such issues arise they should be investigated further using a more advanced analysis. Therefore, to further investigate the observed instability occurring on a large scale at the Ohio mine, FLAC3D numerical models were employed to demonstrate the effect that a weak floor has on roof and pillar stability. This case study will provide important information to limestone mine operators regarding the impact of weak floor causing the potential for roof collapse, pillar failure, and subsequent subsidence of the ground surface.

Nonprescription medication use in patients with heart failure: assessment methods, utilization patterns, and discrepancies with medical records.

BACKGROUND: The use of over-the-counter products, herbals, and vitamins or supplements (collectively termed "nonprescription medications") is common among individuals with cardiovascular disease. We sought to determine patterns and predictors of nonprescription medication use and assessed whether different survey methodology may result in variable patient reporting of these products. METHODS AND RESULTS: We surveyed 161 patients with heart failure. The first 80 participants were provided a written survey to complete during their clinic appointment, and the next 80 age-matched participants met with study personnel for survey administration via face-to-face interview. Over-the-counter product use was reported by 88% of participants, whereas 34.8% took herbal supplements, and 65.2% took vitamins or supplements. Users of nonprescription medications were older, more likely to have an ischemic etiology, and concomitant chronic conditions. No differences in reporting were noted for patient versus provider-administered surveys. Discrepancies between survey and medical record data were common (40.4%), occurring most frequently with nonprescription aspirin, proton pump inhibitors, magnesium, and acetaminophen. CONCLUSIONS: The majority of study participants used nonprescription medications, and often did not report usage to health care providers. Patient education regarding importance of disclosure of nonprescription medications is crucial, as is consistent querying of use by heart failure providers.

The fiscal impact of population aging in Brazil: 2005-2050 / O impacto fiscal do envelhecimento populacional no Brasil: 2005-2050 / El impacto fiscal sobre el envejecimiento de la población en Brasil: 2005-2050

In this paper, we estimate the fiscal impact on population aging in Brazil from 2005 through 2050. We focus on three key areas of public spending: education, pensions, and health care. Our projections are based on a simple model in which aggregate public expenditures are driven by changes in the age structure of the population as well as by changes in the average public benefits received per age. We assess the likely increases in public spending over the coming decades, contrasting the divergent trends in public spending on education, pensions, and health care. We also assess the magnitude of these changes in terms of growth in spending relative to GDP annually through 2050 and estimate the present value of the increase in spending. We find that changing population age structure will lead to increasing cost pressures in health care and especially in pensions. Our projections show that beginning around 2015, public spending will begin a sustained and rapid increase lasting several decades rising from 18% of GDP to reach 27% of GDP by 2050. The needs for increased investment in students will compete against those for sustaining pension benefits and facing increasing demands for health care. In particular, our projections show that an ambitious educational reform aimed at reaching the OECD level of investment per youth within a decade would amount to only about one fourth of the projected cost increases in public health and pension programs.

Neste artigo estimamos a influência do envelhecimento populacional nas despesas em educação, previdência social e saúde no Brasil no período de 2005 a 2050. Os gastos fiscais foram estimados baseados em um modelo simples em que o gasto público agregado é determinado por mudanças na estrutura etária da população e no benefício médio recebido em diferentes idades. Estimamos o provável aumento no gasto público nas próximas décadas, contrastando diferentes tendências no gasto em educação, saúde e previdência social. O valor do crescimento no gasto fiscal é medido em relação ao Produto Interno Bruto (PIB) anual e o valor presente desse aumento em relação ao PIB atual. As projeções mostram que, a partir de 2015, depois de uma pausa, devido às reformas das pensões recentemente promulgadas, a despesa pública iniciará um aumento rápido e sustentado com duração de várias décadas. As necessidades crescentes de investimentos em educação irão competir com o crescente e sustentável aumento de demanda na saúde pública e na previdência social originado do considerável crescimento da proporção de idosos na população brasileira. Em particular, nossas projeções mostram que uma reforma educacional ambiciosa, com o objetivo de alcançar o nível de investimento por jovens em uma década de acordo com a OCDE, equivaleria a apenas cerca de um quarto dos aumentos de custos previstos em programas de saúde pública e de pensões.

En el presente artículo se estima el impacto fiscal sobre el envejecimiento de la población en Brasil entre 2005-2050. Enfocase las tres áreas fundamentales del gasto público: educación, previsión social y salud. Las proyecciones se basan en un modelo simple en el que los gastos públicos agregados son afectados por cambios en la estructura de edad de la población, así como por los cambios en los beneficios públicos promedio recibidos en función de la edad. Evaluase los probables aumentos en el gasto público en las próximas décadas comparando tendencias divergentes en los gastos públicos en las áreas de educación, previsión social y salud. Evaluase la magnitud de estos cambios en lo que se refiere al aumento de los gastos relativos al PIB anual durante los próximos 40 años y estimase el valor presente de este creciente gasto en comparación con el PIB corriente. Verificase que la cambiante estructura de edad de la población ocasionará una presión de aumento en el área de la salud y sobre todo en la seguridad social. Las proyecciones demuestran que alrededor del año de 2015, después de la tregua ocasionada por las reformas recientes en el ámbito de la previsión social, el gasto público iniciará un sostenido y rápido crecimiento durante varias décadas. La necesidad de aumentar las inversiones en el área de la educación competirá con la necesidad de aumentar los beneficios de la previsión social y con crecientes demandas en el sector de la salud, especialmente entre las personas mayores. En particular, nuestras proyecciones indican que una ambiciosa reforma educativa dirigida a alcanzar el nivel de la OCDE de la inversión por la juventud dentro de una década sería de sólo alrededor de una cuarta parte de los aumentos de los costos previstos en los programas de salud pública y de pensiones.

Safety and immunogenicity of bacterial and tobacco plant cell line derived recombinant native and mutant Escherichia coli heat-labile toxin in chickens.

The safety and immunogenicity of the mammalian mucosal adjuvants, Escherichia coli wild-type heat-labile holotoxin (LT) and E. coli mutant LT (LTA-K63/LTB), were examined in 1-day-old chicks and 10-day-old to 21-day-old broilers. Biologically active, E. coli recombinant wild-type LT and recombinant LTA-K63/LTB produced in a transgenic Nicotiana tabacum (NT-1) tobacco cell line (SLT102) were tested for safety and antigenicity following various routes of administration. Safety was assessed by clinical signs, body weight gain, gross organ pathology and wet organ weight, and histopathology. Antigenicity was assessed using LT-B-specific serum IgG enzyme-linked immunosorbent assay. Parenteral administration of E. coli recombinant wild-type LT did not have any discernible effect on bird health and was well tolerated at levels up to 400 µg per dose. Recombinant, SLT102-derived mutant LT derived from SLT102 cells retained in vitro ganglioside binding and was safe and antigenic following repeated mucosal administration to birds. The highest systemic LT-B-specific IgG titres were detected in birds that received three on-feed doses of SLT102-derived mutant LT. Among the various SLT102-derived mutant LT preparations tested, whole, wet cells or whole cell lysates were the most antigenic. These results demonstrate for the first time that E. coli-derived recombinant, wild-type LT holotoxin is well tolerated following multiple administrations to young birds at body weight doses previously reported to be enteropathogenic and toxic in mammalian species. Moreover, these data also demonstrate the feasibility of using recombinant wild-type and mutant LT produced in transgenic NT-1 tobacco cells as safe and potent vaccine adjuvants in poultry.

Automatic discourse connective detection in biomedical text.

OBJECTIVE: Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. MATERIALS AND METHODS: Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (~112,000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (~1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores. RESULTS AND CONCLUSION: Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://bioconn.askhermes.org.